Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

Buettner, Max A.; Monzen, Niklas; Hackl, Christoph M.

doi:10.3390/en15051838

Cited by 17 publications

(16 citation statements)

References 68 publications

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“…The trends observed in the papers published in the journals and proceedings of conferences are focused on artificial intelligence [4][5][6]. A group of the applications is focused on neural networks [7][8][9][10][11].…”

Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

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Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

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Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

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“…O j = exp(I j ) exp(I j ) + exp(−I j ) (20) where the hidden layer input and output denote I j and O j (j = 1, 2, . .…”

Section: The Proposed Rnn For Wind Speed Estimationmentioning

confidence: 99%

“…On the other hand, neural networks (NN) have advantages in universal approximation, quick learning capabilities, parallel computing, and fault tolerance [20]. Thus, the NNbased MPPT control algorithms are most popular in the WECS [21].…”

Section: Introductionmentioning

confidence: 99%

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

2022

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Over the last few years, improving power extraction from the wind energy conversion system (WECS) under varying wind speeds has become a complex task. The current study presents the optimum maximum power point tracking (MPPT) control approach integrated with neural network (NN)-based rotor speed control and pitch angle control to extract the maximum power from the WECS. So, this study presents a reference model adaptive control (RMAC) for a direct-drive (DD) permanent magnet vernier generator (PMVG)-based WECS under real wind speed conditions. Initially, the RMAC-based rotor speed tracking control is presented with adaptive terms, which tracks a reference model that guarantees the expected exponential decay of rotor speed error trajectory. Then, to reduce the wind speed measurement errors, a recurrent neural network (RNN)-based training model is presented. Moreover, the asymptotic stability of the proposed control method is mathematically proven by Lyapunov theory. In addition, the pitch angle control is presented to efficiently operate the rotor speed within the allowable operating range. Eventually, the proposed control system demonstrates its effectiveness through simulation and experimentation using a prototype of 5 kW DD PMVG-based WECS. After that, the comparative results affirm the superiority of the proposed control method over existing control methods.

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“…An ANN-based OFTC for a highly nonlinear anisotropic (I)PMSM is presented in [40]. However, a detailed topology study on how to find an optimal topology with respect to realtime capability, computation time and approximation errors, is still missing.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based optimal feedforward torque control of electrically excited synchronous machines

Monzen¹,

Hackl²

2023

Preprint

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<p>An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider <em>measurable </em>OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time). </p>

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Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

Cited by 17 publications

References 68 publications

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed

Artificial neural network based optimal feedforward torque control of electrically excited synchronous machines

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